What small businesses lose by overlooking big data analytics

Only 12% of SMEs in Asia and Europe incorporate advanced data analytics in their operations.

A study by the Singapore University of Social Sciences (SUSS) reveals that a significant portion of small and medium-sized enterprises (SMEs) are bypassing the benefits of big data analytics, mainly due to resource constraints and limited understanding of the technology.

The report highlights that less than 20% of SMEs actively use big data analytics, despite the growing availability of accessible analytic tools and increasing data volumes.

The SUSS study indicates that adoption rates amongst SMEs remain low, with only about 12% of SMEs in Asia and Europe incorporating advanced data analytics in their operations.

Researchers attribute this gap primarily to cost concerns and lack of expertise. Over half of the SME leaders surveyed by SUSS perceived analytics as prohibitively expensive, whilst nearly 45% cited the absence of skilled personnel as a primary hurdle. The study also noted a significant knowledge gap, with only 15% of surveyed SME managers expressing confidence in their understanding of data analytics applications for their business.

According to SUSS, many SMEs are forgoing substantial cost-saving opportunities by not leveraging data analytics.

The study found that businesses utilising data analytics could reduce operational costs by up to 10%, as the technology enables companies to identify inefficiencies within their supply chains and staffing models. SUSS reported a 5% productivity increase amongst SMEs using data-driven decision-making tools compared to those without, underscoring analytics’ potential to streamline operations and maximise resource allocation.

The report also emphasises the role of big data in optimising customer engagement. According to SUSS findings, businesses that analysed customer data effectively saw improvements in targeting, which translated into 126% profit growth over their non-analytical counterparts. This capability could allow SMEs to narrow the competitive gap with larger businesses, yet, as SUSS points out, limited understanding of data applications remains a critical barrier.

Moreover SUSS data shows that SMEs using customer data analytics reported a notable increase in customer retention, averaging a 10% rise. Customer insights gained through segmentation and predictive modelling allowed these businesses to personalise services and respond more effectively to demand fluctuations.

However, the SUSS study also indicates that only 18% of SMEs feel prepared to gather and apply customer data insights effectively, suggesting significant room for improvement.

The SUSS study also highlights how big data analytics can improve SMEs’ resilience and adaptability, particularly in volatile markets. According to the study, data-driven SMEs were 22% more likely to withstand economic downturns than those that did not use analytics, as the technology allowed them to monitor real-time revenue shifts and adjust strategies accordingly.

For instance, in retail, SUSS data showed that analytics-enabled SMEs experienced a 15% reduction in excess inventory and stockouts, benefiting their cash flow and lowering operational costs.

Growing accessibility of affordable solutions

Whilst many SMEs cited cost as a barrier, the SUSS report points out that recent developments in cloud-based analytics have made data tools more affordable and accessible.

The study found that 40% of SMEs now use low-cost or free cloud-based analytics tools, which offer basic functionality and do not require extensive technical knowledge. According to SUSS, these developments could help bridge the adoption gap by providing SMEs with entry-level options to experiment with data-driven decision-making.

The SUSS report concludes that whilst data analytics offers clear benefits, a majority of SMEs remain unengaged due to cost, skill shortages, and knowledge barriers. The study suggests that overcoming these challenges could be achieved through targeted training programs, industry collaboration, and incentives to make analytics more accessible.

By addressing these obstacles, more SMEs could unlock the potential of big data analytics and strengthen their market position amidst competitive pressures.